Traditional approaches to display datasets in a two-dimensional chart are not well suited for large sets of data. A first approach is called a scatterplot. A scatterplot is a two-dimensional chart that displays each object within the dataset in the two-dimensional chart where the horizontal axis represents one variable of the object and the vertical axis represents a second variable of the object. Each object would be plotted in the scatterplot according to the values of the variables. Since each object within the dataset is represented by a point in the scatterplot, the scatterplot can present thousands of points if the dataset is quite large. These large datasets can be computationally expensive and also clutter the chart making it difficult to extract meaningful information.
A second approach is called a histogram. A histogram is a two-dimensional chart that displays information about the density of a dataset by grouping objects within the dataset into segments. Each segment can be displayed using a color scale to represent the density of the segment (e.g., number of objects within the segment). Through grouping, large datasets can appear less cluttered in the chart since many objects are being clustered together and displayed. However, the inherent nature of the groupings in a histogram prevents critical analysis since objects within the dataset cannot be individually examined.